Jarzyski's Equality and Crooks' Fluctuation Theorem for General Markov Chains with Application to Decision-Making Systems.

We define common thermodynamic concepts purely within the framework of general Markov chains and derive Jarzynski's equality and Crooks' fluctuation theorem in this setup. In particular, we regard the discrete-time case, which leads to an asymmetry in the definition of work that appears in the usual formulation of Crooks' fluctuation theorem. We show how this asymmetry can be avoided with an additional condition regarding the energy protocol. The general formulation in terms of Markov chains allows transferring the results to other application areas outside of physics. Here, we discuss how this framework can be applied in the context of decision-making. This involves the definition of the relevant quantities, the assumptions that need to be made for the different fluctuation theorems to hold, as well as the consideration of discrete trajectories instead of the continuous trajectories, which are relevant in physics.

Assessment of sex- and age-dependency of risk factors for intimal hyperplasia in heart transplant patients using the high resolution of optical coherence tomography.

BACKGROUND: Intimal hyperplasia in cardiac allograft vasculopathy (CAVIH) is limiting survival in pediatric and adult patients after heart transplantation (HTx). Analysis of risk factors for CAVIH using the high resolution of intracoronary optical coherence tomography (OCT) is scarce, particularly in children, and recommendations for CAV prevention are largely based on data obtained in adults. Whether the predictive value of risk factors is age- or sex-dependent is unknown. METHODS AND RESULTS: We used OCT to test the age- and sex-dependency of established risk factors regarding pathological CAVIH in a cohort of 102 pediatric and adult HTx patients (35% <18 years, 69% male). Modifiable parameters such as lipid values, and the diagnoses of dyslipidemia and diabetes showed age- and sex-dependent differences. Regarding CAVIH, receiver-operating characteristic analysis showed that LDL-c was relevant only in female patients (area under the curve [AUC] 0.79, p = 0.007), and total cholesterol in female (AUC 0.81; p = 0.004) and pediatric patients (AUC 0.73, p < 0.05). The association of dyslipidemia with CAVIH was stronger in adult (odds ratio [OR] 6.33) than in pediatric patients (OR 5.00) and in women (OR 6.00) than in men (OR 4.57). Diabetes was associated with CAVIH only in women (OR 11.25). CONCLUSION: In our cohort, modifiable risk factors, particularly total cholesterol and dyslipidemia, had a different impact depending on age and sex. Targeting risk factors in selected patients might improve individual CAVIH prevention.

Nash equilibria in human sensorimotor interactions explained by Q-learning with intrinsic costs.

The Nash equilibrium concept has previously been shown to be an important tool to understand human sensorimotor interactions, where different actors vie for minimizing their respective effort while engaging in a multi-agent motor task. However, it is not clear how such equilibria are reached. Here, we compare different reinforcement learning models to human behavior engaged in sensorimotor interactions with haptic feedback based on three classic games, including the prisoner's dilemma, and the symmetric and asymmetric matching pennies games. We find that a discrete analysis that reduces the continuous sensorimotor interaction to binary choices as in classical matrix games does not allow to distinguish between the different learning algorithms, but that a more detailed continuous analysis with continuous formulations of the learning algorithms and the game-theoretic solutions affords different predictions. In particular, we find that Q-learning with intrinsic costs that disfavor deviations from average behavior explains the observed data best, even though all learning algorithms equally converge to admissible Nash equilibrium solutions. We therefore conclude that it is important to study different learning algorithms for understanding sensorimotor interactions, as such behavior cannot be inferred from a game-theoretic analysis alone, that simply focuses on the Nash equilibrium concept, as different learning algorithms impose preferences on the set of possible equilibrium solutions due to the inherent learning dynamics.

Multi-Modal Pain Intensity Assessment Based on Physiological Signals: A Deep Learning Perspective.

Traditional pain assessment approaches ranging from self-reporting methods, to observational scales, rely on the ability of an individual to accurately assess and successfully report observed or experienced pain episodes. Automatic pain assessment tools are therefore more than desirable in cases where this specific ability is negatively affected by various psycho-physiological dispositions, as well as distinct physical traits such as in the case of professional athletes, who usually have a higher pain tolerance as regular individuals. Hence, several approaches have been proposed during the past decades for the implementation of an autonomous and effective pain assessment system. These approaches range from more conventional supervised and semi-supervised learning techniques applied on a set of carefully hand-designed feature representations, to deep neural networks applied on preprocessed signals. Some of the most prominent advantages of deep neural networks are the ability to automatically learn relevant features, as well as the inherent adaptability of trained deep neural networks to related inference tasks. Yet, some significant drawbacks such as requiring large amounts of data to train deep models and over-fitting remain. Both of these problems are especially relevant in pain intensity assessment, where labeled data is scarce and generalization is of utmost importance. In the following work we address these shortcomings by introducing several novel multi-modal deep learning approaches (characterized by specific supervised, as well as self-supervised learning techniques) for the assessment of pain intensity based on measurable bio-physiological data. While the proposed supervised deep learning approach is able to attain state-of-the-art inference performances, our self-supervised approach is able to significantly improve the data efficiency of the proposed architecture by automatically generating physiological data and simultaneously performing a fine-tuning of the architecture, which has been previously trained on a significantly smaller amount of data.

Value of Echocardiographic Right Ventricular and Pulmonary Pressure Assessment in Predicting Transcatheter Tricuspid Repair Outcome.

OBJECTIVES: The aim of this study was to assess the value of echocardiographic right ventricular (RV) and systolic pulmonary artery pressure (sPAP) assessment in predicting transcatheter tricuspid edge-to-edge valve repair (TTVR) outcome. BACKGROUND: RV dysfunction and pulmonary hypertension are associated with poor prognosis and are systematically sought during tricuspid regurgitation evaluation. The value of echocardiographic assessment in predicting TTVR outcome is unknown. METHODS: Data were taken from the TriValve (Transcatheter Tricuspid Valve Therapies) registry, which includes patients undergoing TTVR at 14 European and North American centers. The primary outcome was 1-year survival free from hospitalization for heart failure, and secondary outcomes were 1-year survival and absence of hospital admission for heart failure at 1 year. RESULTS: Overall, 249 patients underwent TTVR between June 2015 and 2018 (mean tricuspid annular plane systolic excursion [TAPSE] 15.8 ± 15.3 mm, mean sPAP 43.6 ± 16.0 mm Hg). Tricuspid regurgitation grade ≥3+ was found in 96.8% of patients at baseline and 29.4% at final follow-up; 95.6% were in New York Heart Association functional class III or IV initially, compared with 34.3% at follow-up (p < 0.05). Final New York Heart Association functional class did not differ among TAPSE and sPAP quartiles, even when both low TAPSE and high sPAP were present. Rates of 1-year survival and survival free from hospitalization for heart failure were 83.9% and 78.7%, respectively, without significant differences according to baseline echocardiographic RV characteristics (TAPSE, fractional area change, and end-diastolic area) and sPAP (p > 0.05 for all). CONCLUSIONS: TTVR provides clinical improvement, with 1-year survival free from hospital readmission >75% in patients with severe tricuspid regurgitation. Conventional echocardiographic parameters used to assess RV function and sPAP did not predict clinical outcome after TTVR.

Quantifying Motor Task Performance by Bounded Rational Decision Theory.

Expected utility models are often used as a normative baseline for human performance in motor tasks. However, this baseline ignores computational costs that are incurred when searching for the optimal strategy. In contrast, bounded rational decision-theory provides a normative baseline that takes computational effort into account, as it describes optimal behavior of an agent with limited information-processing capacity to change a prior motor strategy (before information-processing) into a posterior strategy (after information-processing). Here, we devised a pointing task where subjects had restricted reaction and movement time. In particular, we manipulated the permissible reaction time as a proxy for the amount of computation allowed for planning the movements. Moreover, we tested three different distributions over the target locations to induce different prior strategies that would influence the amount of required information-processing. We found that movement endpoint precision generally decreases with limited planning time and that non-uniform prior probabilities allow for more precise movements toward high-probability targets. Considering these constraints in a bounded rational decision model, we found that subjects were generally close to bounded optimal. We conclude that bounded rational decision theory may be a promising normative framework to analyze human sensorimotor performance.

Three-dimensional transoesophageal echocardiography for the assessment of clip attachment to the leaflets in percutaneous edge-to-edge repair of the mitral valve.

AIMS: Single leaflet clip attachment (SLA) is a prevalent complication in percutaneous edge-to-edge repair of the mitral valve, leading to the recurrence of significant mitral regurgitation. The objective of this retrospective analysis was to evaluate a novel 3-D transoesophageal echocardiographic method for the assessment of clip attachment to the mitral leaflets. METHODS AND RESULTS: We analysed a total of 87 patients treated for symptomatic mitral regurgitation. In 47 patients, clip attachment to the leaflets was assessed by conventional 2-D transoesophageal echocardiography supported by biplane TEE images (biplane TEE group). In 40 patients, clip attachment to the leaflets was assessed by the intraprocedural 3-D volume method in addition to the conventional method (volumetric TEE group). The primary endpoint was defined as clip complications consisting of SLA and clip displacement at any time after clip implantation. Clip complications occurred in nine patients (19.1%) in the biplane TEE group and in two patients (5%) in the volumetric TEE group (p=0.06). Regarding the grade of mitral regurgitation, in the follow-up period we observed a more pronounced deterioration in the biplane TEE group than in the volumetric TEE group. CONCLUSIONS: These findings suggest that the additional use of 3-D volumetric transoesophageal echocardiography for the assessment of clip attachment to the mitral leaflets may contribute to a reduced rate of subsequent clip complications.